RICERCA ITALIANA     

Similar FIRB:

• 1 - Redesigning the financial infrastructure of networks of companies: in search of new financial, institutional and IT solutions to support competitiveness, innovation, corporate reorganization and risk management.
• 2 - Bioinformatics for Genomics and Proteomics
• 3 - Agile methodologies for software development - validation, modelling, impact on firm organization, their use for the distributed development and for the development of open source software
• 4 - e-Government for Mediterranean countries (eG4M) - New methodologies in sociological, economic, organizational and technological areas for e-model adaptation and enhancement in Mediterranean Countries, conception and guidelines for strategic plans, experimentation of guidelines in tourism, small and medium enterprise services, awareness and dissemination of the enhanced e-model, and access to public data services.

Scientific and education field classification

• Field: Scienze economiche e statistiche
  • ECONOMIA AZIENDALE
  • ECONOMIA E GESTIONE DELLE IMPRESE

Geographical classification

• Region: Lombardia

Bibliografia

DATA MINING METHODOLOGIES
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. and Verkamo, A.I. (1995). Fast discovery of association rules. In: Advances in knowledge discovery and data mining. AAAI/MIT Press, Cambridge.
Berry, M. and Linoff, G. (1997). Data Mining techniques for marketing, sales, and customer support. Wiley, New York.
Berry, M. and Linoff, G. (2000). Mastering data mining. Wiley, New York.
Breiman, L., Friedman, J.H., Olshen, R. and Stone, C. J. (1984). Classification and regression trees. Wadsworth, Belmont
Giudici, P. (2003), Applied Data Mining: statistical methods for business and industry, Wiley, London.
Han J., Kamber M. (2000), Data Mining: Concepts and Tecniques, Morgan Kaufmann.
Hand D. J., Heikki M., Smyth P. (2001), Principles of Data Mining, MIT Press.
Hastie, T., Tibshirani, R., Friedman, J. (2001), The elements of statistical learning: data mining, inference and prediction. Springer-Verlag.
Heckerman, D., (1997) Bayesian Networks for Data Mining. Journal of Data Mining and Knowledge Discovery 1, pp. 79-119.
Ripley, B.D. (1996). Pattern recognition and neural networks. Cambridge University Press, Cambridge.
Scarpa B., Torelli N. (2003) Selecting the training set in classification problems with rare events, Atti del convegno CLADAG 2003, Bologna, 24-27 settembre 2003
Giudici, P. (2002). Statistical models for data mining. special issue of Italian Journal of Applied Statistics.
Giudici, P., Heckerman, D. e Whittaker, J. (2001). Statistical models for data mining. Special issue of: Journal of Knowledge discovery and data mining.
Vapnik, V. (1998). Statistical learning theory. Wiley, New York.
DATA ACQUISITION AND DATA MANAGEMENT
Baker K., Harris P., O’Brien J. (1989) Data Fusion: An Appraisal and Experimental Evaluation. Journal of the Market Research Society, 31 (2), 152-212.
Giudici, P., Schoier G. (2001) Cluster analysis for web usage mining. Italian Journal of Applied statistics.
Japkowicz, N. (2000) The class imbalance problem: Significance and strategies,Proceedings of the 2000 International Conference on Artificial Intelligence, 111–117.
Jephcott, J., Bock T. (1998). The application and validation of data fusion. Journal of the Market Research Society, vol 40, nr 3 (July), p. 185-205.
Joshi M. (2002) On evaluating performance of classifiers for rare classes, url:http//wwwusers.cs.umn.edu/ mjoshi/papers/icdm02sub.ps.
Lenz H.J., (1998), “Multi data sources and data fusion”, Proceedings III International Seminar on New Techniques and new technology for Statistics, Sorrento, Italy, 139-146.
Ling C.X. and Li C. (1998) Data mining for direct marketing: problems and solutions,Proceedings of the Int. Conference on Knowledge Discovery and Data Mining.
Raessler, S. (2002), Statistical Matching: frequentist theory, practical applications and alternative bayesian approaches”, Springer-Verlag, New York.
Scarpa B., Torelli N. (2003), “Selecting the training set in classification problems with rare events” in Book of Short Papers, Cladag 2003, Bologna 22-24 settembre, Ed. Clueb, Bologna, pp.317-320
Winkler W. E (2000) “Machine Learning, Information Retrieval, and Record Linkage” Proceedings of the American Statistical Association, Survey Research Methods Section
FINANCIAL RISK MANAGEMENT
Cruz, M. (2002). Modelling, measuring and hedging operational risk. Wiley, London
De Giuli, M, Maggi, A. e Paris, F. (2003) Pricing mutual bank deposit guarantees. Proceedings of the 10th conference of the multinational finance society.
De Giuli, M, Maggi, A. e Paris, F. (2003) Pricing incentive fee of hedge fund managers: a discussion of moral hazard. Proceedings of the 11th conference of the multinational finance society.
e Maggi, M. Derivati: teoria e metodi. Giappichelli, Torino.
Giudici, P. e Polasek, W. (2001) Inference and prediction on Financial risk management. Special issue of: Applied Stochastic Models in Business and Industry.
Rossi E., C.Zucca (2002) "Hedging Interest Rate Risk with Multivariate GARCH”, Applied Financial Economics, 12, 241-251.
INTERNATIONALISATION AND BUSINESS FINANCE
European Commission, Observatoire européen des PME, Sixieme Rapport, Luxembourg, 2000.
Johanson J. & Vahlne J.E. (1977), The internationalisation process of the firm. A model of knowledge development and increasing market commitment, Journal of International Business, vol. 8, 23 – 32
Johanson J. & Vahlne J.E (1990), The mechanism of internationalisation, International Marketing Review, vol. 7, 11- 24
Leonidou L.C. and C. S. Katsikeas (1996), The Export Development Process: an Integrative Review of Empirical Models, Journal of International Business, 6, pp.121-148
Dimitratos, et al. (2003), Micromultinationals: New Types of Firms for the Global Competitive Landscape, European Management Journal, Vol. 21, No. 2 pp., 164-174
Majocchi A. and Zucchella A. (2003), Internationalization and Performance. Findings from a Set of Italian SMEs, International Small Business Journal, 21(3), 249–266
OECD (1997),Globalisation and Small and Medium Enterprises (SMEs), OECD; Paris
WEB KNOWLEDGE DISCOVERY AND CRM
Baldi, P., Frasconi, P. e Smyth, P. (2003) Modelling the internet and the web. Wiley, New York.
Chakrabarti, S. (2003) Mining the web: discovering knowledge from hypertext data. Morgan Kaufmann, New York.
Blanc, E. e Giudici, P. (2002) Association and classification models for web mining. In: Data Mining III (a cura di A. Zanasi, C.A. Brebbia, N.F.F.E. Ebecken and P. Melli), WIT Press, pp. 937-946.
Blanc, E. e Giudici, P. (2002) Sequence rules for web clickstream analysis. In: Advances in Data Mining (a cura di P. Perner), Springer Verlag, pp. 1-14
Giudici, P. (2004). Statistical models for web usage mining. To appear in Applied stochastic models in business and industry.
Greenberg, P. (2000) CRM at the speed of light: capturing and keeping customer in Internet real time, McGraw Hill
Normann, R., Ramirez, R. (1994) Designing interactive strategy. From value chain to value constellation, John Wiley & Sons Ltd.
Tourniaire, F., (2003) Just enough C.R.M., McGraw Hill.

Keywords

data management; knowledge management; financial risk management; strategic assessment software for SMEs; SMEs internationalisation; web based customer relationship management